SoroushVahidi/scidocs huggingface dataset:
Box Pleating is Hard
(2015)

In their seminal 1996 paper, Bern and Hayes initiated investigation into the computational complexity of origami [3]. They proved that it is NP-hard to determine whether a given general crease pattern can be folded flat, when the creases have or have not been assigned crease directions (mountain fold or valley fold). Since that time, there has been considerable work in analyzing the computational complexity of other origami related problems. For example, Arkin et al. [1] proved that deciding foldability is hard even for simple folds, while Demaine et al. [4] proved that optimal circle packing for origami design is also hard. At the end of their paper, Bern and Hayes pose some interesting open questions to further their work. While most of them have been investigated since, two in particular (problems 2 and 3) have remained untouched until now. First, while the gadgets used in their hardness proof for unassigned crease patterns are relatively straightforward, their gadgets for assigned crease patterns are considerably more convoluted, and quite difficult to check. In particular, we found an error in their crossover gadget where signals are not guaranteed to transmit dependably for wires that do not cross orthogonally, which is required in their construction. Is there a simpler way to achieve a correct result (i.e. “without tabs”)? Second, their reductions construct creases at a variety of unconstrained angles. Is deciding flat foldability easy under more restrictive inputs? For example box pleating, folding only along creases aligned at multiples of 45◦ to each other, is a subset of particular interest in transformational robotics and selfassembly, with a universality result constructing arbitrary polycubes using box pleating [2]. In this paper we prove deciding flat foldability of box pleated crease patterns to be NP-hard in both the unassigned and assigned cases, using relatively simple gadgets containing no more than 20 layers at any point. A crease pattern is a straight-line graph of creases on a square paper which all must be folded by ±180◦ resulting in a flat folding, a piecewise isometry in the plane such that the paper does not intersect itself. We call a crease a valley fold if it folds
Valuing American Options by Simulation : A Simple Least-Squares Approach
(2000)

This article presents a simple yet powerful new approach for approximating the value of American options by simulation. The key to this approach is the use of least squares to estimate the conditional expected payoff to the optionholder from continuation. This makes this approach readily applicable in path-dependent and multifactor situations where traditional finite difference techniques cannot be used. We illustrate this technique with several realistic examples including valuing an option when the underlying asset follows a jump-diffusion process and valuing an American swaption in a 20-factor string model of the term structure.
Occlusion Culling in OpenSG PLUS
(2004)

Very large polygonal models are frequently used in scientific computing, mechanical engineering, or virtual medicine. An effective technique to handle these large models is occlusion culling. Like most large model techniques, occlusion culling trades overhead costs with the rendering costs of the possibly occluded geometry. In this paper, we present occlusion culling techniques for OpenSG. These techniques utilize OpenGL or OpenGL extensions to determine occlusion information. Techniques utilizing image space and temporal coherence are used to optimize occlusion query efficiency. Only the standard OpenSG data structures are used and no precomputation is necessary for the presented techniques.
Analyzing (social media) networks with NodeXL
(2009)

We present NodeXL, an extendible toolkit for network overview, discovery and exploration implemented as an add-in to the Microsoft Excel 2007 spreadsheet software. We demonstrate NodeXL data analysis and visualization features with a social media data sample drawn from an enterprise intranet social network. A sequence of NodeXL operations from data import to computation of network statistics and refinement of network visualization through sorting, filtering, and clustering functions is described. These operations reveal sociologically relevant differences in the patterns of interconnection among employee participants in the social media space. The tool and method can be broadly applied.
In Search of the Uncanny Valley
(2009)

Recent advances in computer animation and robotics have lead to greater and greater realism of human appearance to be obtained both on screen and in physical devices. A particular issue that has arisen in this pursuit is whether increases in realism necessarily lead to increases in acceptance. The concept of the uncanny valley suggests that high, though not perfect, levels of realism will result in poor acceptance. We review this concept and its psychological basis.
Timeline: A Dynamic Hierarchical Dirichlet Process Model for Recovering Birth/Death and Evolution of Topics in Text Stream
(2010)

Topic models have proven to be a useful tool for discovering latent structures in document collections. However, most document collections often come as temporal streams and thus several aspects of the latent structure such as the number of topics, the topics’ distribution and popularity are time-evolving. Several models exist that model the evolution of some but not all of the above aspects. In this paper we introduce infinite dynamic topic models, iDTM, that can accommodate the evolution of all the aforementioned aspects. Our model assumes that documents are organized into epochs, where the documents within each epoch are exchangeable but the order between the documents is maintained across epochs. iDTM allows for unbounded number of topics: topics can die or be born at any epoch, and the representation of each topic can evolve according to a Markovian dynamics. We use iDTM to analyze the birth and evolution of topics in the NIPS community and evaluated the efficacy of our model on both simulated and real datasets with favorable outcome.
Robust control of uncertain Markov Decision Processes with temporal logic specifications
(2012)

We present a method for designing a robust control policy for an uncertain system subject to temporal logic specifications. The system is modeled as a finite Markov Decision Process (MDP) whose transition probabilities are not exactly known but are known to belong to a given uncertainty set. A robust control policy is generated for the MDP that maximizes the worst-case probability of satisfying the specification over all transition probabilities in this uncertainty set. To this end, we use a procedure from probabilistic model checking to combine the system model with an automaton representing the specification. This new MDP is then transformed into an equivalent form that satisfies assumptions for stochastic shortest path dynamic programming. A robust version of dynamic programming solves for a ε-suboptimal robust control policy with time complexity O(log1/ε) times that for the non-robust case.
Enforcing k-anonymity in Web Mail Auditing
(2016)

We study the problem of k-anonymization of mail messages in the realistic scenario of auditing mail traffic in a major commercial Web mail service. Mail auditing is necessary in various Web mail debugging and quality assurance activities, such as anti-spam or the qualitative evaluation of novel mail features. It is conducted by trained professionals, often referred to as "auditors", who are shown messages that could expose personally identifiable information. We address here the challenge of k-anonymizing such messages, focusing on machine generated mail messages that represent more than 90% of today's mail traffic. We introduce a novel message signature Mail-Hash, specifically tailored to identifying structurally-similar messages, which allows us to put such messages in a same equivalence class. We then define a process that generates, for each class, masked mail samples that can be shown to auditors, while guaranteeing the k-anonymity of users. The productivity of auditors is measured by the amount of non-hidden mail content they can see every day, while considering normal working conditions, which set a limit to the number of mail samples they can review. In addition, we consider k-anonymity over time since, by definition of k-anonymity, every new release places additional constraints on the assignment of samples. We describe in details the results we obtained over actual Yahoo mail traffic, and thus demonstrate that our methods are feasible at Web mail scale. Given the constantly growing concern of users over their email being scanned by others, we argue that it is critical to devise such algorithms that guarantee k-anonymity, and implement associated processes in order to restore the trust of mail users.
Descriptive versus interpretive phenomenology: their contributions to nursing knowledge.
(2004)

A number of articles in the nursing literature discuss the differences between descriptive and interpretive approaches to doing phenomenology. A review of studies demonstrates, however, that many researchers do not articulate which approach guides the study, nor do they identify the philosophical assumptions on which the study is based. Such lack of clarity makes it difficult for the reader to obtain a sense of how the knowledge produced by the study is to be evaluated and used. In this article, the authors compare the philosophical components of descriptive and interpretive approaches to doing phenomenology and illustrate types of knowledge produced by each through reviewing specific studies. They focus on the various uses of phenomenology in generating useful knowledge for health care practice.
SQLEM: Fast Clustering in SQL using the EM Algorithm
(2000)

Clustering is one of the most important tasks performed in Data Mining applications. This paper presents an efficient SQL implementation of the EM algorithm to perform clustering in very large databases. Our version can effectively handle high dimensional data, a high number of clusters and more importantly, a very large number of data records. We present three strategies to implement EM in SQL: horizontal, vertical and a hybrid one. We expect this work to be useful for data mining programmers and users who want to cluster large data sets inside a relational DBMS.
Author Identification Using Imbalanced and Limited Training Texts
(2007)

This paper deals with the problem of author identification. The common N-grams (CNG) method [6] is a language-independent profile-based approach with good results in many author identification experiments so far. A variation of this approach is presented based on new distance measures that are quite stable for large profile length values. Special emphasis is given to the degree upon which the effectiveness of the method is affected by the available training text samples per author. Experiments based on text samples on the same topic from the Reuters Corpus Volume 1 are presented using both balanced and imbalanced training corpora. The results show that CNG with the proposed distance measures is more accurate when only limited training text samples are available, at least for some of the candidate authors, a realistic condition in author identification problems.
Bigrams of Syntactic Labels for Authorship Discrimination of Short Texts
(2007)

We present a method for authorship discrimination that is based on the frequency of bigrams of syntactic labels that arise from partial parsing of the text. We show that this method, alone or combined with other classification features, achieves a high accuracy on discrimination of the work of Anne and Charlotte Brontë, which is very difficult to do by traditional methods. Moreover, high accuracies are achieved even on fragments of text little more than 200 words long. .................................................................................................................................................................................
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
(1997)

The Rocchio relevance feedback algorithm is one of the most popular and widely applied learning methods from information retrieval. Here, a probabilistic analysis of this algorithm is presented in a text categorization framework. The analysis gives theoretical insight into the heuristics used in the Rocchio algorithm, particularly the word weighting scheme and the similarity metric. It also suggests improvements which lead to a probabilistic variant of the Rocchio classi er. The Rocchio classi er, its probabilistic variant, and a naive Bayes classi er are compared on six text categorization tasks. The results show that the probabilistic algorithms are preferable to the heuristic Rocchio classi er not only because they are more well-founded, but also because they achieve better performance.
Multiple nutrient deficiency detection in paddy leaf images using color and pattern analysis
(2016)

Paddy being the staple food of India is majorly affected by deficiency of primary nutrient elements like nitrogen, phosphorus and potassium. Leaves can be deficient with multiple nutrient elements at a same time. This can alter natural color of paddy leaves. Such leaves are considered as defective. The proposed work is to automate multiple nutrient element deficiency identification of paddy leaves. Pattern analysis RGB color features are extracted to identify defective paddy leaves. Firstly the database of healthy, nitrogen, phosphorus and potassium defected paddy leaves are created. For any test image effective comparison at different levels are employed such as multiple color comparison, multiple pattern comparison and combination of color and patterns comparison, so that defectiveness is accurately identified for combination of deficiency such as nitrogen-phosphorus(NP), nitrogen-potassium(NK) and phosphorous-potassium (KP).
Facial Landmark Detection with Tweaked Convolutional Neural Networks
(2018)

This paper concerns the problem of facial landmark detection. We provide a unique new analysis of the features produced at intermediate layers of a convolutional neural network (CNN) trained to regress facial landmark coordinates. This analysis shows that while being processed by the CNN, face images can be partitioned in an unsupervised manner into subsets containing faces in similar poses (i.e., 3D views) and facial properties (e.g., presence or absence of eye-wear). Based on this finding, we describe a novel CNN architecture, specialized to regress the facial landmark coordinates of faces in specific poses and appearances. To address the shortage of training data, particularly in extreme profile poses, we additionally present data augmentation techniques designed to provide sufficient training examples for each of these specialized sub-networks. The proposed Tweaked CNN (TCNN) architecture is shown to outperform existing landmark detection methods in an extensive battery of tests on the AFW, ALFW, and 300W benchmarks. Finally, to promote reproducibility of our results, we make code and trained models publicly available through our project webpage.
Undervalued or Overvalued Customers : Capturing Total Customer Engagement Value
(2010)

Customers can interact with and create value for firms in a variety of ways. This article proposes that assessing the value of customers based solely upon their transactions with a firm may not be sufficient, and valuing this engagement correctly is crucial in avoiding undervaluation and overvaluation of customers. We propose four components of a customer’s engagement value (CEV) with a firm. The first component is customer lifetime value (the customer’s purchase behavior), the second is customer referral value (as it relates to incentivized referral of new customers), the third is customer influencer value (which includes the customer’s behavior to influence other customers, that is increasing acquisition, retention, and share of wallet through word of mouth of existing customers as well as prospects), and the fourth is customer knowledge value (the value added to the firm by feedback from the customer). CEV provides a comprehensive framework that can ultimately lead to more efficient marketing strategies that enable higher long-term contribution from the customer. Metrics to measure CEV, future research propositions regarding relationships between the four components of CEV are proposed and marketing strategies that can leverage these relationships suggested.
Wordnet Enhanced Automatic Crossword Generation
(2006)

We report on a system for automatically generating and displaying crosswords from a system manager supplied database of potential clues and corresponding words that index those clues. The system relies on the lexical relations encoded in WordNet to enhance the aesthetics of the resulting crossword by making it easier to automatically identify a grid that may be populated with words and clues that have a thematic focus. The system architecture is provided in overview, as is empirical evaluation.
Software evolution storylines
(2010)

This paper presents a technique for visualizing the interactions between developers in software project evolution. The goal is to produce a visualization that shows more detail than animated software histories, like code_swarm [15], but keeps the same focus on aesthetics and presentation. Our software evolution storylines technique draws inspiration from XKCD's "Movie Narrative Charts" and the aesthetic design of metro maps. We provide the algorithm, design choices, and examine the results of using the storylines technique. Our conclusion is that the it is able to show more details when compared to animated software project history videos. However, it does not scale to the largest projects, such as Eclipse and Mozilla.
Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph
(2018)

While conversing with chatbots, humans typically tend to ask many questions, a significant portion of which can be answered by referring to large-scale knowledge graphs (KG). While Question Answering (QA) and dialog systems have been studied independently, there is a need to study them closely to evaluate such real-world scenarios faced by bots involving both these tasks. Towards this end, we introduce the task of Complex Sequential QA which combines the two tasks of (i) answering factual questions through complex inferencing over a realistic-sized KG of millions of entities, and (ii) learning to converse through a series of coherently linked QA pairs. Through a labor intensive semi-automatic process, involving in-house and crowdsourced workers, we created a dataset containing around 200K dialogs with a total of 1.6M turns. Further, unlike existing large scale QA datasets which contain simple questions that can be answered from a single tuple, the questions in our dialogs require a larger subgraph of the KG. Specifically, our dataset has questions which require logical, quantitative, and comparative reasoning as well as their combinations. This calls for models which can: (i) parse complex natural language questions, (ii) use conversation context to resolve coreferences and ellipsis in utterances, (iii) ask for clarifications for ambiguous queries, and finally (iv) retrieve relevant subgraphs of the KG to answer such questions. However, our experiments with a combination of state of the art dialog and QA models show that they clearly do not achieve the above objectives and are inadequate for dealing with such complex real world settings. We believe that this new dataset coupled with the limitations of existing models as reported in this paper should encourage further research in Complex Sequential QA.
Can we learn to gamble efficiently
(2010)

Betting is an important problem faced by millions of sports fans each day. Presented with an upcoming matchup between team A and team B, and given the opportunity to place a 50/50 wager on either, where should a gambler put her money? This decision is not, of course, made in isolation: both teams will have played a number of decided matches with other teams throughout the season. Furthermore, a reasonable assumption to make is that the relation “A tends to beat B” is transitive. Under transitivity, the best prediction strategy is clearly to sort the teams by their abilities and predict according to this ranking. The obvious difficulty is that the best ranking of the teams is not known an advance. But there’s a more subtle issue: even with knowledge of all match outcomes in advance, i.e. a list of items of the form (team X < team Y ), it’s NP-hard to determine the best ranking of the teams when the outcomes are noisy. This is exactly the infamous Minimum Feedback Arc Set problem. The question we pose is as follows: can we design a non-trivial online prediction strategy in this setting which achieves vanishing regret relative to the best ranking in hindsight, even when the latter is computationally infeasible? It is tempting to believe this is impossible, as competing with the best ranking would appear tantamount to finding the best ranking. However, this assertion is false: the algorithm need not learn a ranking explicitly, it must only output a prediction (X < Y ) or (Y < X) when presented with a pair (X, Y ), and these predictions do not necessarily have to satisfy transitivity. Indeed, consider the following simple algorithm: treat each team pair (X, Y ) as an independent learning problem (ignoring all other matchups). In the long run, this will achieve vanishing regret with respect to the best ranking. So why is this not desirable? The trivial approach unfortunately admits a bad regret bound: the algorithm must see O(n) matches per team before it can start to make decent predictions. On the other hand, there is an information-theoretic approach that requires only O(log n) observations per team—the downside, unfortunately, is that this requires exponential computation. We would like to achieve this rate with an efficient method.
Towards Building Forensics Enabled Cloud Through Secure Logging-as-a-Service
(2016)

Collection and analysis of various logs (e.g., process logs, network logs) are fundamental activities in computer forensics. Ensuring the security of the activity logs is therefore crucial to ensure reliable forensics investigations. However, because of the black-box nature of clouds and the volatility and co-mingling of cloud data, providing the cloud logs to investigators while preserving users' privacy and the integrity of logs is challenging. The current secure logging schemes, which consider the logger as trusted cannot be applied in clouds since there is a chance that cloud providers (logger) collude with malicious users or investigators to alter the logs. In this paper, we analyze the threats on cloud users' activity logs considering the collusion between cloud users, providers, and investigators. Based on the threat model, we propose Secure-Logging-as-a-Service ( SecLaaS), which preserves various logs generated for the activity of virtual machines running in clouds and ensures the confidentiality and integrity of such logs. Investigators or the court authority can only access these logs by the RESTful APIs provided by SecLaaS, which ensures confidentiality of logs. The integrity of the logs is ensured by hash-chain scheme and proofs of past logs published periodically by the cloud providers. In prior research, we used two accumulator schemes Bloom filter and RSA accumulator to build the proofs of past logs. In this paper, we propose a new accumulator scheme - Bloom-Tree, which performs better than the other two accumulators in terms of time and space requirement.